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Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

Chao Han1, Leanna House1, Scotland C Leman1

  • 1Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America.

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Summary
This summary is machine-generated.

Generative Topographic Mapping (GTM) is enhanced for high-dimensional data visualization. A new Visual to Parametric Interaction (V2PI) framework improves GTM

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Area of Science:

  • Machine Learning
  • Data Visualization
  • Artificial Intelligence

Background:

  • Generative Topographic Mapping (GTM) is a nonlinear latent variable model for high-dimensional data visualization.
  • Traditional GTM methods face challenges with complex parameterization, leading to fitting difficulties and model sensitivity.
  • Existing linear methods often fail to capture intricate data structures effectively.

Purpose of the Study:

  • To extend Generative Topographic Mapping (GTM) into an interactive visual analytics framework.
  • To enable user-guided parameterization and multi-perspective data assessment within GTM.
  • To introduce a dynamic GTM version, V2PI-GTM, fostering enhanced data exploration.

Main Methods:

  • Development of Visual to Parametric Interaction (V2PI) theory and methods for GTM.
  • Integration of user interaction into the GTM parameterization process.
  • Implementation of a dynamic framework for GTM visualizations.

Main Results:

  • Successful development of the V2PI-GTM framework.
  • Demonstrated ability of V2PI-GTM to facilitate guided data exploration.
  • Validation of V2PI-GTM benefits through a text mining case study.

Conclusions:

  • V2PI-GTM offers a more robust and user-friendly approach to high-dimensional data visualization.
  • The interactive framework addresses limitations of traditional GTM, improving model fitting and stability.
  • V2PI-GTM enhances data exploration capabilities, particularly in complex domains like text mining.